Beyond the Buzz: Strategic Paths for Enabling Useful NISQ Applications

Quantum computing is conceived as a promising and powerful next-generation platform for information processing and it has been shown that it could bring significant accelerations to certain tasks, compared to its classical counterparts. With recent advances in noisy intermediate-scale quantum (NISQ) devices, we can process classical data from real-world problems using hybrid quantum systems. In this work, we investigate the critical problem of designing a gate-based hybrid quantum neuron under NISQ constraints to enable the construction of scalable hybrid quantum deep neural networks (HQDNNs). We explore and characterize diverse quantum circuits for hybrid quantum neurons and discuss related critical components of HQDNNs. We also utilize a new schema to infer multiple predictions from a single hybrid neuron. We further compose a highly customizable platform for simulating HQDNNs via Qiskit and test them on diverse classification problems including the iris and the wheat seed datasets. The results show that even HQDNNs with the simplest neurons could lead to superior performance on these tasks. Finally, we show that the HQDNNs are robust to certain levels of noise, making them preferred on NISQ devices. Our work provides a comprehensive investigation of building scalable near-term gate-based HQDNNs and paves the way for future studies of quantum deep learning via both simulations on classical computers and experiments on accessible NISQ devices.

The increase in quantum algorithm development in recent years has spurred the development of new algorithms which outsource classically intractable portions of an algorithm to a quantum computer. These hybrid quantum-classical algorithms must be thoroughly studied and analyzed to determine their viability. One such algorithm is the hybrid quantum-classical convolutional neural network. In this paper, we compared different implementations of hybrid quantum-classical convolutional neural networks and their fully classical counterparts. In particular, we compared their accuracy and loss performance over time. To do this, we implemented both the classical and hybrid quantum-classical algorithms with varying convolutional layers and ran them on a cluster of classical computers and IBM’s cloud quantum computers using the low-dimensional Tetris dataset. We found that hybrid quantum-classical convolutional neural networks do not outperform their classical counterparts in terms of accuracy and loss over time when tested on current-state quantum computers, which conflicts with past work. We can attribute the discrepancy to the noise present in current quantum computers affecting the results of the circuit. KeywordsMachine learningQuantum computingConvolutional neural networks

In recent years, quantum computers and algorithms have made significant progress indicating the prospective importance of quantum computing (QC). Especially combinatorial optimization has gained a lot of attention as an application field for near-term quantum computers, both by using gate-based QC via the Quantum Approximate Optimization Algorithm and by quantum annealing using the Ising model. However, demonstrating an advantage over classical methods in real-world applications remains an active area of research. In this work, we investigate the robustness verification of ReLU networks, which involves solving many-variable mixed-integer programs (MIPs), as a practical application. Classically, complete verification techniques struggle with large networks as the combinatorial space grows exponentially, implying that realistic networks are difficult to be verified by classical methods. To alleviate this issue, we propose to use QC for neural network verification and introduce a hybrid quantum procedure to compute provable certificates. By applying Benders decomposition, we split the MIP into a quadratic unconstrained binary optimization and a linear program which are solved by quantum and classical computers, respectively. We further improve existing hybrid methods based on the Benders decomposition by reducing the overall number of iterations and placing a limit on the maximum number of qubits required. We show that, in a simulated environment, our certificate is sound, and provides bounds on the minimum number of qubits necessary to approximate the problem. Finally, we evaluate our method within simulations and on quantum hardware.

Two-qubit entangling gates for superconducting quantum computers

Beginning with Feynman in 1982, it was proposed that highly controllable quantum devices, later to become known as quantum computers, would be especially good at the simulation of other quantum systems. Around the turn of the 21st century, this notion was later formalized (by Lloyd and coworkers at MIT and Laflamme and coworkers at Los Alamos) to show how in particular instances, we expect an exponential speedup in the solution of the Schroedinger equation for chemical systems. Subsequently, quantum-accelerated quantum simulation for chemistry techniques have been developed by IBM, Google, Harvard, etc. This effort has led to the OpenFermion (openfermion.org, OF) community, an open source effort for compiling and analyzing quantum algorithms to simulate fermionic systems, including quantum chemistry. Among other functionalities, the current version of OF features data structures and tools for obtaining and manipulating representations of fermionic and qubit Hamiltonians. In October, 2017, the cover of Chemical and Engineering News posited “Quantum computers: Chemistry could be the machine’s killer app” for first principles understanding of biological systems and complex materials. Prof. Aspuru-Guzik (Harvard) predicts “I think that before 2035 QC will be beating classical computers at chemistry.” The algorithms at the heart of quantum chemistry codes (eigensolvers, fast fourier transforms) are the computation bottleneck.

The purpose of the study is to assess the application of Computer science in the fractional distillation of petroleum. The study sought to: Determine the merit of using Computer science in fractional distillation of petroleum. Evaluate the cost effectiveness to the application of Computer science in fractional distillation of petroleum to the welfare of citizens. Determine if Computer science can help in controlling the natural effects of fractional distillation of petroleum on the environment. Computer science has gained wider application in so many areas of human endeavor ranging from the homes to more scientific and industrial applications, hence its application of Computer science in fractional distillation of petroleum cannot be overemphasized. Irrespective of the benefits of the application of Computer science in fractional distillation of petroleum as a whole, it has become pertinent to ask how effective and efficient its application of Computer science in fractional distillation of petroleum has been. This research work tries to investigate how effective the application of Computer science in fractional distillation of petroleum. The project exertion covered statement of the problem, purpose of the study, research questions/hypotheses were formulated to enable the researcher find out facts about the study, highlighted in division one. Subdivision two was based on the literature review of this study. The study covers the research methodology which guided the study, the next stage was based on the presentation of data and its’ analysis as well as the discussion of findings. The final stage of the research was concerned with the conclusion as well as recommendations and suggestions for further studies

As quantum computing development advances closer toward achieving fault-tolerant error-corrected realisation, debates on applications, impacts, risks, and benefits of quantum computing are timely and due. While there is awareness of the potential power and complexity of quantum computers, there has been relatively little attention on the social impacts and ethical implications of this technology. In grappling with the social impacts of quantum computing, some stakeholders have turned to applied ethics, specifically (classical) computer and Artificial Intelligence (AI) ethics, for guidance. While computer and AI ethics are useful starting points for evaluating the ethical issues posed by quantum computing, uncritically applying existing sets of ethical principles risks overlooking how quantum computing differs from these technologies. We argue that borrowing of ethical principles and guidelines from AI and computing is inappropriate for several reasons: (1) quantum computing, classical computing, and AI are different technologies with significant material differences; (2) unlike AI and classical computers which have become established, quantum computing is an emerging technology, which has implications on the levels of accessibility and immediate impact in society; and (3) there are significant differences in the way AI and quantum computing have been developed and by whom. We also briefly summarise some of the unique risks and societ al impacts posed by quantum computing. We posit that these reasons support the argument for a new sub-field of quantum computing ethics, which would allow a relevant scholarship to develop and provide guidance as quantum computing technology continues to mature.

Both scientific and industrial applications require temperature stabilization and enforcement for testing purposes. In this study, we present a solution capable of handling socket-based IC test systems enabling packages from QFN up to FCBGA, or even COB solutions. The temperature range covers the full-range industrial temperature range (−40 °C to +125 °C). The extended temperature range of −55 °C to +150 °C is conditionally possible. Solution supports dry-air installation, safety mechanisms and flexible thermal head assemblies. We present the key features and architecture of the solution named “Thermal Elephant” that found applications in the industrial (characterization of the IP hard macros) and scientific applications (radiation imaging ICs).

3 - Charge-coupled device (CCD) image sensors

The development of near-term quantum computers, referred to as Noisy Intermediate-Scale Quantum (NISQ) computers, has progressed rapidly in the past few years resulting in several quantum computers which vary in their underlying technology and physical constraints. The performance of these computers also varies from one quantum algorithm to another. To enable efficient selection of the quantum computer that provides the highest output fidelity for a given application, an accurate noise modeling of each quantum hardware is required. However, noise modeling for a given application is a complex problem because of the unknown interaction between the quantum circuit parameters and the noise parameters of NISQ devices. We propose the use of Machine Learning (ML) to model the performance of different quantum computers at the application level. The ML models predict the output fidelity of the quantum application executed on different quantum computers given their publicly available physical constraints. We use a diverse training dataset to cover the key features for application-level benchmarking of the quantum hardware. Our results obtained from different superconducting quantum devices show that our proposed ML models enable cost-effective quantum computer selection for different quantum applications with different fidelity metrics.

In the world of computation, the invention of Computers has reduced human effort for accomplishing task. Depending on the type, size and nature of the work the computers are manufactured in different magnitudes. As the technology grows, it facilitated to manufacture high performing, high throughput, and high memory Classical computers. Classical computers store any information in the form of bits in the memory device, which can be either 1 or 0 (binary system) and all the processing will be done in the same logic i.e., high voltage/low voltage. Because of this logic the classical computers solve some problems by using more resources. Quantum computer are the next level computers which uses quantum theory principles for solving problems. The quantum computer uses Qubit as basic unit which uses principles of quantum computing such as superposition and entanglement, which gives qubit, the efficiency of exhibiting many logical states at a time. This paper shows a comparative study on classical and quantum computers and illustrates various problems in classical computers and respective solutions with use of quantum computers.

Positioning systems play an important role in industrial and scientific applications, making their performance improvements and functional expansions crucial. Positioning systems generally consist of mechanisms, actuators, controllers, and sensors, with performances and functions depending on their integration. Accordingly, positioning technologies focus on these elements and their integration into the system. Actuators are fundamental and critical elements that characterize positioning systems. Electromagnetic actuators are the most widely used because of their high basic performance, and piezoelectric actuators are often used for fine movement. In addition, research on new actuators with unique characteristics is actively being pursued. In component integration, control technology plays a crucial role in effectively utilizing the characteristics of elements and addressing their drawbacks. With the recent advances in digital technology facilitating nonlinear representations and structural modifications, various control methods have been proposed and evaluated to benefit from these advances. In addition to proposing modeling methods, the effects of manufacturing processes on system performance have been evaluated. This special issue contains 18 papers covering the design and characteristics of mechanisms and actuators, control technology, system design, and modeling, along with three review papers on positioning technology and its applications. We would like to express our sincere gratitude to the authors for their excellent contributions to the special issue. We also thank the reviewers for their incisive efforts in editing this special issue. We hope that this special issue will help readers enhance their knowledge and understanding of positioning technology and its applications, thereby contributing to its progress.

Two-phase flow detection, particularly gas-liquid flow, is critical in various industrial applications such as oil and gas pipelines, chemical processing, and biomedical systems. Conventional techniques, including ultrasonic, optical, and microwave-based methods, often face limitations in terms of resolution, penetration, and sensitivity. Terahertz (THz) imaging systems offer a promising non-invasive alternative due to their high spatial resolution, ability to penetrate non-metallic materials, and sensitivity to variations in refractive index and dielectric properties. This study explores the feasibility of using a THz imaging system for detecting and characterizing gas-liquid two-phase flow. A continuous-wave (CW) THz imaging system has been employed to capture high-resolution images of flow channels within transparent and semi-transparent conduits. By analyzing the THz image acquired from the reflected signals, distinct phase distributions and flow patterns have been identified. The experimental setup includes a controlled test rig with varying gas-to-liquid ratios to simulate different flow regimes, namely, slug, discontinued slug, etc. Image processing techniques have been applied to enhance phase differentiation and improve detection accuracy. Preliminary results demonstrate that THz imaging can effectively distinguish gas and liquid phases based on their absorption and reflection characteristics. The proposed method provides a non-intrusive, high-precision approach to monitoring two-phase flow, offering significant advantages over traditional sensors. Future work will focus on real-time implementation, optimizing imaging parameters, and extending the methodology to optically-opaque pipelines. This research contributes to advancing flow diagnostics in industrial and scientific applications.

Quantum computers perform computations exploiting quantum mechanics to a possible advantage, allowing us to prepare and manipulate states that do not have a classical equivalent. In particular, phenomena like superposition and entanglement may enable quantum computers to outperform their classical counterparts in certain applications. Implementing these useful quantum algorithms is contingent upon building accurate quantum hardware that is not affected by noise. Environmental noise decreases coherence time of qubits, meaning that qubits do not stay in a desired state long enough to carry out a complex computation. To that end, harnessing the full power of quantum computers necessitate characterization of noise sources and how they impact a given quantum system. Often times, T1and T2 are used to quantify noise. In this project, we provide an approach as to how T1 and T2 values are calculated and simulated for quantum systems. In addition, we compare simulated values of T1 and T2 with those of real quantum computer’s measurements. IBMQ Experience, an open source software allowing users to simulate and use real quantum hardware, is used. QuTip, a Python-based toolbox offering quantum simulation tools for open quantum systems, is also used.

Quantum computing is a multidisciplinary field at the intersectionof quantum physics, computer science, and mathematics, aiming to har-ness quantum mechanical phenomena for computational advantage. Thisarticle provides a comprehensive review of both the foundational princi-ples and advanced theoretical models of quantum computing. We beginwith the basic framework of quantum computation, introducing the con-cept of qubits as two-level quantum systems, the linear algebra of Hilbertspaces, Dirac’s bra–ket notation, and the postulates of quantum mechan-ics (state superposition, unitary evolution, and projective measurement).We then discuss quantum logic gates and circuits, highlighting how clas-sical reversible computation principles extend to universal quantum gatesets. Next, we survey key quantum algorithms (from Shor’s factoring toGrover’s searching and beyond) and the complexity-theoretic implicationsof quantum computers, contrasting the class BQP with classical complex-ity classes. We cover the theoretical foundations of quantum error cor-rection and fault tolerance, explaining how quantum information can beprotected from decoherence using redundancy and how fault-tolerant pro-tocols can, in principle, allow scalable quantum computation despite noisyhardware. We then explore advanced models and paradigms of quantumcomputation, including measurement-based quantum computing (one-wayquantum computing), topological quantum computation with anyons, adi-abatic quantum computing and quantum annealing, continuous-variablequantum computing, and related approaches. We also provide an overviewof quantum information theory as it relates to computation, discussingentanglement, quantum entropy, and information-theoretic limits like theHolevo bound. Finally, we review the leading physical implementations(quantum hardware models) of quantum computers—trapped ions, super-conducting circuits, photonic systems, solid-state spin qubits, and oth-ers—outlining the challenges and achievements of each approach.